{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:58:30Z","timestamp":1780102710468,"version":"3.54.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Defense grant","award":["DoD HT9425-23-1-0003"],"award-info":[{"award-number":["DoD HT9425-23-1-0003"]}]},{"name":"Cancer Center Support Grant","award":["P30CA068485"],"award-info":[{"award-number":["P30CA068485"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P01 AI139449, R01 DK135597"],"award-info":[{"award-number":["P01 AI139449, R01 DK135597"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Cancer Institute grants","award":["U2C CA233291, U54 CA217450, P01CA229123 and U54 CA274367"],"award-info":[{"award-number":["U2C CA233291, U54 CA217450, P01CA229123 and U54 CA274367"]}]},{"name":"Vanderbilt Medical Center Department of Biostatistics Development Award"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Spatial transcriptomics (ST) integrates gene expression data with the spatial organization of cells and their associated histology, offering unprecedented insights into tissue biology. While existing methods incorporate either location-based or histology-informed information, none fully synergize gene expression, histological features, and precise spatial coordinates within a unified framework. Moreover, these methods often exhibit inconsistent performance across diverse datasets and conditions. Here, we introduce stImage, an open-source R package that provides a comprehensive and flexible solution for ST analysis. By generating deep learning\u2013derived histology features and offering 54 integrative strategies, stImage seamlessly combines transcriptional profiles, histology images, and spatial information. We demonstrate stImage\u2019s effectiveness across multiple datasets, underscoring its ability to guide users toward the most suitable integration strategy using diagnostic graph. Our results highlight how stImage can optimize ST, consistently improving biological insights and advancing our understanding of tissue architecture. stImage is freely available at https:\/\/github.com\/YuWang-VUMC\/stImage.<\/jats:p>","DOI":"10.1093\/bib\/bbaf429","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T11:35:27Z","timestamp":1754393727000},"source":"Crossref","is-referenced-by-count":2,"title":["stImage: a versatile framework for optimizing spatial transcriptomic analysis through customizable deep histology and location informed integration"],"prefix":"10.1093","volume":"26","author":[{"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Vanderbilt University Medical Center , 2525 West End Avenue, Suite 1100, Nashville, TN 37232 ,","place":["United States"]},{"name":"Center for Quantitative Sciences, Vanderbilt University Medical Center , 2525 West End Avenue, Suite 1020, Nashville, TN 37232 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